Duplicate from Adit-jain/Soccana_player_ball_detection_v1

Co-authored-by: Adit Jain <Adit-jain@users.noreply.huggingface.co>

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+# Audio files - uncompressed
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README.md

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+---
+task_categories:
+- object-detection
+language:
+- en
+tags:
+- soccer
+- football
+- player
+- referee
+- detection
+- ball
+- ultralytics
+- yolov11
+- tracking
+pretty_name: Soccana_prb_v1
+size_categories:
+- 10K<n<100K
+---
+
+# ⚽ Soccer Object Detection Dataset (25K Subset from 1M+ Images)
+
+---
+
+## Index
+
+1. [Dataset Overview](#dataset-overview)
+2. [Folder Structure](#folder-structure)
+3. [Dataset Preparation](#dataset-preparation)
+4. [Data Utils](#data-utils)
+5. [Samples](#samples)
+
+## Dataset Overview
+
+This dataset is a curated subset (25,000 images) from a larger soccer vision dataset containing over **1 million images** (50+ GB). The data was collected and augmented from multiple **open-source sources**, including the **SoccerNet dataset**, video game renders, and publicly available match footage.
+
+It is optimized for **object detection tasks**, especially focusing on soccer-related entities such as **players**, **referees**, and the **ball**, including various augmentation types like background-only and noisy scenes.
+
+
+- ✅ 25,000 images (~1.5GB)
+- ✅ Annotations for 3 object classes:
+  - `player`
+  - `referee`
+  - `ball`
+- ✅ Data format:
+  - **Ultralytics YOLO format** (default)
+  - **COCO JSON format** (included in separate folders)
+- ✅ Resolution variety:
+  - `160x160`
+  - `320x320`
+  - `640x640`
+  - `1280x1080` (Full HD)
+- The dataset includes frames for various scenarios, such as:
+  - Occlusions
+  - Close up shots
+  - Behind the goalpost scenes
+  - Camera overlay scenes
+  - Low and High angle shots
+  - Low resolution shots
+- ### Classes
+  | Class ID | Label |
+  | -------- | ------- |
+  | 0 | Player |
+  | 1 | Referee |
+  | 2 | Ball |
+
+In all, the dataset provides a apt starting point for an all rounder football object detection model.
+
+---
+
+## 🗂️ Folder Structure
+
+```
+V1/
+├── images/
+│ ├── train/
+│ └── test/
+├── labels/ # YOLO TXT labels
+│ ├── train/
+│ └── test/
+├── coco_test_annotations/ # COCO format labels (train.json, val.json)
+├── coco_train_annotations/ # COCO format labels (train.json, val.json)
+├── data.yaml # Ultralytics YOLOv8-compatible YAML
+└── samples/ # Dataset samples
+```
+
+---
+
+## Dataset Preparation
+
+### Processing Pipeline Architecture
+
+```
+Raw COCO Datasets
+        ↓
+SAHI Slicing (160/320/640/1280)
+        ↓
+Image Limit and Filtering
+        ↓
+Class Name Standardization
+        ↓
+COCO to YOLO Conversion
+        ↓
+Final Training Dataset
+```
+
+### Raw COCO Datasets:
+The following datasets were used for the raw images
+1. **[Soccer Player Tracker](https://universe.roboflow.com/sac-wjhag/soccer-player-tracker)** (`spt_v2`)
+2. **[Football Detection Test](https://universe.roboflow.com/projet-m2/test-fooball-detection-bis)** (`tbd_v2`)
+3. **[VA Project](https://universe.roboflow.com/vaa/va_project-mp2xn)** (`v2_temp`)
+4. **[Player Detection GKLRL](https://universe.roboflow.com/wisd-ckexz/player-detection-gklrl)** (`v12`)
+5. **[Football EITPT](https://universe.roboflow.com/va-sah7v/football-eitpt)** (`v5_temp`)
+6. **[Detect Players DGXZ0](https://universe.roboflow.com/nikhil-chapre-xgndf/detect-players-dgxz0)** (`v3`)
+7. **[Football Player Detection KUCAB](https://universe.roboflow.com/augmented-startups/football-player-detection-kucab)** (`v7`)
+8. **[Football Players Detection 3ZVBC](https://universe.roboflow.com/roboflow-jvuqo/football-players-detection-3zvbc)**
+
+
+### SAHI slicing
+SAHI (Slicing Aided Hyper Inference) is implemented to handle the multi-scale nature of soccer scenes:
+
+**Why SAHI for Soccer?**
+- **Crowded Scenes**: Penalty area situations with multiple overlapping players
+- **Scale Variation**: Players appear at different sizes based on camera distance
+- **Small Object Detection**: Ball detection in wide-angle shots
+- **Context Preservation**: Maintains spatial relationships through overlapping
+
+```python
+slice_sizes = [160, 320, 640, 1280]  # Multiple scale processing
+overlap_ratio = 0.2            # 20% overlap between patches
+```
+
+- **160x160 patches**: Optimized for small player detection and crowded scenes
+- **320x320 patches**: Balanced approach for medium-distance shots  
+- **640x640 patches**: Preserves context for tactical analysis and large-scale scenes
+- **640x640 patches**: For best results in HD context
+
+
+### Image Limit and Filtering
+Due to SAHI, the resulting dataset had 1M+ images, and more than 30GB of data. Image filtering was applied from each dataset
+```python
+# Per-dataset image limits for balanced training
+image_limits = {
+    "spt_v2": 30, "spt_v2_sahi_160": 30, "spt_v2_sahi_320": 40,
+    "tbd_v2": -1, "v2_temp": 300, "v2_temp_sahi_160": 300,
+    "v2_temp_sahi_320": 400, "v3": 500, "v3_sahi_160": 500,
+    "v3_sahi_320": 1000, "v3_sahi_640": 500, "v5_temp": 500,
+    "v7": 500, "v7_sahi_160": 500, "v7_sahi_320": 1000,
+    "v7_sahi_640": 500, "v12": 200, "v12_sahi_160": 300,
+    "v12_sahi_320": 500, "v12_sahi_640": 300,
+}
+```
+
+
+### Class name standardization
+Every dataset had different classes, hence three common classes were taken out from each sub dataset
+- **Player Variants**: Maps 'Player', 'Team-A', 'Team-H', 'football player', 'goalkeeper', 'Gardien', 'Joueur' → Class 0
+- **Ball Variants**: Maps 'ball', 'Ball', 'Ballon', 'football' → Class 1  
+- **Referee Variants**: Maps 'referee', 'Referee', 'Arbitre' → Class 2
+
+
+### COCO to YOLO
+the final COCO format dataset was converted to YOLO format fro ultralytics pipeline. Both the formats can be found in the zip file.
+
+---
+
+## Data Utils
+
+### **Processing Scripts Location**
+
+All dataset processing utilities are available in the **Data_utils** directory:
+
+**🔗 Repository Link**: [https://github.com/Adit-jain/Soccer_Analysis/tree/main/Data_utils](https://github.com/Adit-jain/Soccer_Analysis/tree/main/Data_utils)
+
+### **Key Utilities**
+
+#### **External_Detections/**
+- **`slice_images.py`**: SAHI-based multi-scale slicing
+- **`merge_datasets.py`**: Multi-dataset integration with class mapping
+- **`coco_to_yolo.py`**: Format conversion with coordinate normalization
+- **`create_data_yaml.py`**: YOLO training configuration generation
+- **`visualize_coco_dataset.py`**: Quality control and visualization
+
+#### **SoccerNet_Detections/**
+- **`get_soccernet_data.py`**: SoccerNet dataset downloading
+- **`data_preprocessing.py`**: MOT to YOLO conversion pipeline
+
+---
+
+
+## Samples
+<p align="center">
+  <img src="samples/Figure_1.png" width="800"/>
+  <img src="samples/Figure_2.png" width="800"/>
+  <img src="samples/Figure_3.png" width="800"/>
+</p>
+
+<p align="center">
+  <img src="samples/Figure_4.png" width="800"/>
+  <img src="samples/Figure_5.png" width="800"/>
+  <img src="samples/Figure_6.png" width="800"/>
+</p>
+
+<p align="center">
+  <img src="samples/Figure_7.png" width="800"/>
+  <img src="samples/Figure_8.png" width="800"/>
+  <img src="samples/Figure_9.png" width="800"/>
+</p>
+
+<p align="center">
+  <img src="samples/Figure_10.png" width="800"/>
+</p>

V1.zip

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+size 1341080234